The Application of Visualization Technology on Knowledge Management
ABSTRACT The application of the visualization technology on the knowledge management can be addressed in three aspects: the visualization for knowledge discovery, the visualization of knowledge representations and the visualization of information organization. The visualization for knowledge discovery refers to discover knowledge by visualization techniques based on analyzing and processing original data. The visualization of knowledge representations includes visually display the knowledge and rules discovered by data mining and other knowledge discovery techniques for making knowledge facilitate understanding. The Conceptual Map, Cognitive Map and Mind Map are useful explorations of visualization technology in the research field of knowledge organization.
- 3 01/2008; Course Technology.
Conference Proceeding: Knowledge Discovery in Databases: An Overview.[show abstract] [hide abstract]
ABSTRACT: Data Mining and knowledge Discovery in Databases (KDD) promise to play an important role in the way people interact with databases, especially decision support databases where analysis and exploration operations are essential. Inductive logic programming can potentially play some key roles in KDD. This is an extended abstract for an invited talk in the conference. In the talk, we define the basic notions in data mining and KDD, define the goals, present motivation, and give a high-level definition of the KDD Process and how it relates to Data Mining. We then focus on data mining methods. Basic coverage of a sampling of methods will be provided to illustrate the methods and how they are used. We cover a case study of a successful application in science data analysis: the classification of cataloging of a major astronomy sky survey covering 2 billion objects in the northern sky. The system can outperform human as well as classical computational analysis tools in astronomy on the task of recognizing faint stars and galaxies. We also cover the problem of scaling a clustering problem to a large catalog database of billions of objects. We conclude with a listing of research challenges and we outline area where ILP could play some important roles in KDD.Inductive Logic Programming, 7th International Workshop, ILP-97, Prague, Czech Republic, September 17-20, 1997, Proceedings; 01/1997
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ABSTRACT: The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.01/2007; Springer., ISBN: 978-1-84628-765-7